Cognitive Computer Science Research Group

We are a research group within the AI laboratory. Our name,
CCSRG, is meant to indicate that while our approach is primarily
computational, we are informed by a cognitive
perspective on both artificial and natural systems.

The focus of
our research group is the characterization of adaptive knowledge
representations. Issues of representation have always played a
central role in artificial intelligence (AI), as well as in computer
science and theories of mind more generally. We would argue that most
of this work has (implicitly or explicitly) assumed that the
representational language is wielded manually, by humans
encoding an explicit characterization of what they believe to be true
of the world. We believe there are fundamental philosophical
difficulties inherent in any such approach. Further, there now exist
modern machine learning techniques capable of automatically
developing elaborate representations of the world. To date, however,
the representations underlying this learning have not shown themselves
able to "scale up" to the semantically sophisticated task
domains often associated with AI expert systems. We believe it is
therefore appropriate to reconsider basic notions of what makes for
good knowledge representation, with constraints imposed by the
learning process considered sine qua non but in conjuction
with others (expressive adequacy, valid inference, etc.) more
typically considered by AI.

We have found it productive to pursue
this general interest through several more specific research projects.
The first applies statistical techniques to the problem of free-text
information retrieval (IR) and linguistics more generally. Many of
our projects use a connectionist (neural network) representation of
documents and descriptive keywords that uses relevance feedback as a
training signal to a reinforcement learning algorithm. This
construction allows an IR system to learn a more effective indexing
representation of free-text documents as a simple by-product of the
browsing behaviors of its users. Second, we have investigated a wide
range of Genetic Algorithm (GAs) applications, ranging from use in
"artificial life" models of natural phenomena to use as an
artificial inductive method to accomplish an engineering goal like
optimizing a function. We believe our work in these two areas allows
a ``stereoscopic'' view of cognitive adaptation, encompassing a broad
range of fundamental issues from low-level, biological constraints to
high-level, symbolic communication.